Overview

Dataset statistics

Number of variables39
Number of observations1176
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory247.0 KiB
Average record size in memory215.1 B

Variable types

Numeric11
Categorical28

Alerts

Age is highly overall correlated with TotalWorkingYearsHigh correlation
MonthlyIncome is highly overall correlated with TotalWorkingYears and 3 other fieldsHigh correlation
TotalWorkingYears is highly overall correlated with Age and 3 other fieldsHigh correlation
YearsInCurrentRole is highly overall correlated with YearsSinceLastPromotionHigh correlation
YearsSinceLastPromotion is highly overall correlated with YearsInCurrentRoleHigh correlation
JobLevel is highly overall correlated with MonthlyIncome and 2 other fieldsHigh correlation
StockOptionLevel is highly overall correlated with MaritalStatus_SingleHigh correlation
BusinessTravel_Travel_Frequently is highly overall correlated with BusinessTravel_Travel_RarelyHigh correlation
BusinessTravel_Travel_Rarely is highly overall correlated with BusinessTravel_Travel_FrequentlyHigh correlation
EducationField_Life Sciences is highly overall correlated with EducationField_MedicalHigh correlation
EducationField_Medical is highly overall correlated with EducationField_Life SciencesHigh correlation
JobRole_Manager is highly overall correlated with MonthlyIncome and 2 other fieldsHigh correlation
JobRole_Research Director is highly overall correlated with MonthlyIncomeHigh correlation
MaritalStatus_Married is highly overall correlated with MaritalStatus_SingleHigh correlation
MaritalStatus_Single is highly overall correlated with StockOptionLevel and 1 other fieldsHigh correlation
EducationField_Marketing is highly imbalanced (51.4%)Imbalance
EducationField_Other is highly imbalanced (68.5%)Imbalance
EducationField_Technical Degree is highly imbalanced (56.6%)Imbalance
JobRole_Human Resources is highly imbalanced (79.4%)Imbalance
JobRole_Manager is highly imbalanced (63.5%)Imbalance
JobRole_Manufacturing Director is highly imbalanced (52.7%)Imbalance
JobRole_Research Director is highly imbalanced (69.5%)Imbalance
JobRole_Sales Representative is highly imbalanced (68.8%)Imbalance
NumCompaniesWorked has 158 (13.4%) zerosZeros
TrainingTimesLastYear has 41 (3.5%) zerosZeros
YearsInCurrentRole has 193 (16.4%) zerosZeros
YearsSinceLastPromotion has 459 (39.0%) zerosZeros

Reproduction

Analysis started2023-02-06 11:28:49.222158
Analysis finished2023-02-06 11:30:16.541373
Duration1 minute and 27.32 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Age
Real number (ℝ)

Distinct43
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.846088
Minimum18
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2023-02-06T06:30:16.953510image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q130
median35
Q343
95-th percentile54
Maximum60
Range42
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.0386942
Coefficient of variation (CV)0.24530946
Kurtosis-0.34525265
Mean36.846088
Median Absolute Deviation (MAD)6
Skewness0.42932542
Sum43331
Variance81.697993
MonotonicityNot monotonic
2023-02-06T06:30:17.677679image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
34 66
 
5.6%
35 62
 
5.3%
32 55
 
4.7%
29 54
 
4.6%
31 53
 
4.5%
38 52
 
4.4%
33 51
 
4.3%
36 50
 
4.3%
40 48
 
4.1%
30 48
 
4.1%
Other values (33) 637
54.2%
ValueCountFrequency (%)
18 6
 
0.5%
19 6
 
0.5%
20 9
 
0.8%
21 11
 
0.9%
22 13
 
1.1%
23 13
 
1.1%
24 21
1.8%
25 19
1.6%
26 30
2.6%
27 36
3.1%
ValueCountFrequency (%)
60 5
 
0.4%
59 9
0.8%
58 10
0.9%
57 4
 
0.3%
56 6
 
0.5%
55 17
1.4%
54 14
1.2%
53 15
1.3%
52 15
1.3%
51 15
1.3%

DailyRate
Real number (ℝ)

Distinct791
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean802.04592
Minimum103
Maximum1499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2023-02-06T06:30:18.091068image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum103
5-th percentile169.5
Q1466.75
median799.5
Q31154
95-th percentile1418.5
Maximum1499
Range1396
Interquartile range (IQR)687.25

Descriptive statistics

Standard deviation401.1665
Coefficient of variation (CV)0.50017897
Kurtosis-1.2022479
Mean802.04592
Median Absolute Deviation (MAD)343.5
Skewness0.0065894886
Sum943206
Variance160934.56
MonotonicityNot monotonic
2023-02-06T06:30:18.501615image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
691 6
 
0.5%
329 5
 
0.4%
530 5
 
0.4%
267 4
 
0.3%
589 4
 
0.3%
465 4
 
0.3%
408 4
 
0.3%
430 4
 
0.3%
921 4
 
0.3%
1146 4
 
0.3%
Other values (781) 1132
96.3%
ValueCountFrequency (%)
103 1
 
0.1%
104 1
 
0.1%
105 1
 
0.1%
106 1
 
0.1%
107 1
 
0.1%
109 1
 
0.1%
111 3
0.3%
115 1
 
0.1%
116 2
0.2%
117 3
0.3%
ValueCountFrequency (%)
1499 1
 
0.1%
1498 1
 
0.1%
1496 2
0.2%
1495 3
0.3%
1492 1
 
0.1%
1490 3
0.3%
1488 1
 
0.1%
1485 1
 
0.1%
1482 1
 
0.1%
1480 2
0.2%

DistanceFromHome
Real number (ℝ)

Distinct29
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2593537
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2023-02-06T06:30:18.993764image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q314
95-th percentile26
Maximum29
Range28
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.188622
Coefficient of variation (CV)0.88436215
Kurtosis-0.27737605
Mean9.2593537
Median Absolute Deviation (MAD)5
Skewness0.94742099
Sum10889
Variance67.053529
MonotonicityNot monotonic
2023-02-06T06:30:19.297524image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2 170
14.5%
1 163
13.9%
10 70
 
6.0%
3 70
 
6.0%
7 69
 
5.9%
9 61
 
5.2%
8 59
 
5.0%
4 53
 
4.5%
5 51
 
4.3%
6 51
 
4.3%
Other values (19) 359
30.5%
ValueCountFrequency (%)
1 163
13.9%
2 170
14.5%
3 70
6.0%
4 53
 
4.5%
5 51
 
4.3%
6 51
 
4.3%
7 69
5.9%
8 59
 
5.0%
9 61
 
5.2%
10 70
6.0%
ValueCountFrequency (%)
29 22
1.9%
28 20
1.7%
27 10
0.9%
26 21
1.8%
25 22
1.9%
24 21
1.8%
23 21
1.8%
22 15
1.3%
21 15
1.3%
20 23
2.0%

Education
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
3
458 
4
319 
2
230 
1
133 
5
 
36

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row3
4th row3
5th row4

Common Values

ValueCountFrequency (%)
3 458
38.9%
4 319
27.1%
2 230
19.6%
1 133
 
11.3%
5 36
 
3.1%

Length

2023-02-06T06:30:20.086913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T06:30:20.370340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
3 458
38.9%
4 319
27.1%
2 230
19.6%
1 133
 
11.3%
5 36
 
3.1%

Most occurring characters

ValueCountFrequency (%)
3 458
38.9%
4 319
27.1%
2 230
19.6%
1 133
 
11.3%
5 36
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 458
38.9%
4 319
27.1%
2 230
19.6%
1 133
 
11.3%
5 36
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 458
38.9%
4 319
27.1%
2 230
19.6%
1 133
 
11.3%
5 36
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 458
38.9%
4 319
27.1%
2 230
19.6%
1 133
 
11.3%
5 36
 
3.1%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
4
379 
3
354 
2
223 
1
220 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row1
5th row2

Common Values

ValueCountFrequency (%)
4 379
32.2%
3 354
30.1%
2 223
19.0%
1 220
18.7%

Length

2023-02-06T06:30:20.666753image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T06:30:21.016117image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
4 379
32.2%
3 354
30.1%
2 223
19.0%
1 220
18.7%

Most occurring characters

ValueCountFrequency (%)
4 379
32.2%
3 354
30.1%
2 223
19.0%
1 220
18.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 379
32.2%
3 354
30.1%
2 223
19.0%
1 220
18.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 379
32.2%
3 354
30.1%
2 223
19.0%
1 220
18.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 379
32.2%
3 354
30.1%
2 223
19.0%
1 220
18.7%

Gender
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
1
705 
0
471 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 705
59.9%
0 471
40.1%

Length

2023-02-06T06:30:21.317900image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T06:30:21.621973image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1 705
59.9%
0 471
40.1%

Most occurring characters

ValueCountFrequency (%)
1 705
59.9%
0 471
40.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 705
59.9%
0 471
40.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 705
59.9%
0 471
40.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 705
59.9%
0 471
40.1%

HourlyRate
Real number (ℝ)

Distinct71
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.235544
Minimum30
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2023-02-06T06:30:21.944776image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile33
Q148
median66
Q384
95-th percentile97
Maximum100
Range70
Interquartile range (IQR)36

Descriptive statistics

Standard deviation20.459483
Coefficient of variation (CV)0.30888978
Kurtosis-1.2119347
Mean66.235544
Median Absolute Deviation (MAD)18
Skewness-0.071664223
Sum77893
Variance418.59043
MonotonicityNot monotonic
2023-02-06T06:30:22.407953image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98 24
 
2.0%
96 24
 
2.0%
48 24
 
2.0%
87 23
 
2.0%
42 23
 
2.0%
57 23
 
2.0%
84 23
 
2.0%
54 22
 
1.9%
79 21
 
1.8%
73 20
 
1.7%
Other values (61) 949
80.7%
ValueCountFrequency (%)
30 17
1.4%
31 12
1.0%
32 18
1.5%
33 15
1.3%
34 9
0.8%
35 16
1.4%
36 15
1.3%
37 15
1.3%
38 11
0.9%
39 15
1.3%
ValueCountFrequency (%)
100 16
1.4%
99 13
1.1%
98 24
2.0%
97 16
1.4%
96 24
2.0%
95 17
1.4%
94 18
1.5%
93 15
1.3%
92 20
1.7%
91 17
1.4%

JobInvolvement
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
3
702 
2
295 
4
112 
1
 
67

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row3
4th row3
5th row2

Common Values

ValueCountFrequency (%)
3 702
59.7%
2 295
25.1%
4 112
 
9.5%
1 67
 
5.7%

Length

2023-02-06T06:30:22.911301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T06:30:23.317039image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
3 702
59.7%
2 295
25.1%
4 112
 
9.5%
1 67
 
5.7%

Most occurring characters

ValueCountFrequency (%)
3 702
59.7%
2 295
25.1%
4 112
 
9.5%
1 67
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 702
59.7%
2 295
25.1%
4 112
 
9.5%
1 67
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 702
59.7%
2 295
25.1%
4 112
 
9.5%
1 67
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 702
59.7%
2 295
25.1%
4 112
 
9.5%
1 67
 
5.7%

JobLevel
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
2
438 
1
423 
3
175 
4
84 
5
56 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 438
37.2%
1 423
36.0%
3 175
 
14.9%
4 84
 
7.1%
5 56
 
4.8%

Length

2023-02-06T06:30:23.603047image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T06:30:23.934418image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
2 438
37.2%
1 423
36.0%
3 175
 
14.9%
4 84
 
7.1%
5 56
 
4.8%

Most occurring characters

ValueCountFrequency (%)
2 438
37.2%
1 423
36.0%
3 175
 
14.9%
4 84
 
7.1%
5 56
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 438
37.2%
1 423
36.0%
3 175
 
14.9%
4 84
 
7.1%
5 56
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 438
37.2%
1 423
36.0%
3 175
 
14.9%
4 84
 
7.1%
5 56
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 438
37.2%
1 423
36.0%
3 175
 
14.9%
4 84
 
7.1%
5 56
 
4.8%

MonthlyIncome
Real number (ℝ)

Distinct1090
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6525.2526
Minimum1009
Maximum19999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2023-02-06T06:30:24.307954image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1009
5-th percentile2098.5
Q12900.25
median4938.5
Q38480.5
95-th percentile17798.25
Maximum19999
Range18990
Interquartile range (IQR)5580.25

Descriptive statistics

Standard deviation4726.3655
Coefficient of variation (CV)0.72431917
Kurtosis0.98919581
Mean6525.2526
Median Absolute Deviation (MAD)2221.5
Skewness1.3685422
Sum7673697
Variance22338531
MonotonicityNot monotonic
2023-02-06T06:30:24.718415image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2342 4
 
0.3%
3452 3
 
0.3%
5562 3
 
0.3%
6347 3
 
0.3%
2451 3
 
0.3%
2886 2
 
0.2%
5238 2
 
0.2%
2564 2
 
0.2%
2028 2
 
0.2%
2693 2
 
0.2%
Other values (1080) 1150
97.8%
ValueCountFrequency (%)
1009 1
0.1%
1052 1
0.1%
1129 1
0.1%
1200 1
0.1%
1223 1
0.1%
1232 1
0.1%
1359 1
0.1%
1393 1
0.1%
1416 1
0.1%
1420 1
0.1%
ValueCountFrequency (%)
19999 1
0.1%
19973 1
0.1%
19926 1
0.1%
19859 1
0.1%
19845 1
0.1%
19833 1
0.1%
19740 1
0.1%
19717 1
0.1%
19701 1
0.1%
19665 1
0.1%

MonthlyRate
Real number (ℝ)

Distinct1147
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14372.792
Minimum2097
Maximum26999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2023-02-06T06:30:25.117128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2097
5-th percentile3440.5
Q18222
median14379.5
Q320444.25
95-th percentile25426.5
Maximum26999
Range24902
Interquartile range (IQR)12222.25

Descriptive statistics

Standard deviation7072.7447
Coefficient of variation (CV)0.49209262
Kurtosis-1.2040512
Mean14372.792
Median Absolute Deviation (MAD)6085
Skewness0.018691872
Sum16902403
Variance50023717
MonotonicityNot monotonic
2023-02-06T06:30:25.503711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9150 3
 
0.3%
15318 2
 
0.2%
11591 2
 
0.2%
12355 2
 
0.2%
20364 2
 
0.2%
4156 2
 
0.2%
11162 2
 
0.2%
9096 2
 
0.2%
25326 2
 
0.2%
23016 2
 
0.2%
Other values (1137) 1155
98.2%
ValueCountFrequency (%)
2097 1
0.1%
2104 1
0.1%
2112 1
0.1%
2122 1
0.1%
2125 2
0.2%
2137 1
0.1%
2261 1
0.1%
2323 1
0.1%
2326 1
0.1%
2338 1
0.1%
ValueCountFrequency (%)
26999 1
0.1%
26997 1
0.1%
26968 1
0.1%
26956 1
0.1%
26933 1
0.1%
26914 1
0.1%
26897 1
0.1%
26849 1
0.1%
26841 1
0.1%
26820 1
0.1%

NumCompaniesWorked
Real number (ℝ)

Distinct10
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6879252
Minimum0
Maximum9
Zeros158
Zeros (%)13.4%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2023-02-06T06:30:25.840120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4744599
Coefficient of variation (CV)0.92058364
Kurtosis0.025929728
Mean2.6879252
Median Absolute Deviation (MAD)1
Skewness1.0237581
Sum3161
Variance6.1229519
MonotonicityNot monotonic
2023-02-06T06:30:26.078836image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 406
34.5%
0 158
 
13.4%
3 138
 
11.7%
2 124
 
10.5%
4 105
 
8.9%
6 61
 
5.2%
7 56
 
4.8%
5 49
 
4.2%
8 41
 
3.5%
9 38
 
3.2%
ValueCountFrequency (%)
0 158
 
13.4%
1 406
34.5%
2 124
 
10.5%
3 138
 
11.7%
4 105
 
8.9%
5 49
 
4.2%
6 61
 
5.2%
7 56
 
4.8%
8 41
 
3.5%
9 38
 
3.2%
ValueCountFrequency (%)
9 38
 
3.2%
8 41
 
3.5%
7 56
 
4.8%
6 61
 
5.2%
5 49
 
4.2%
4 105
 
8.9%
3 138
 
11.7%
2 124
 
10.5%
1 406
34.5%
0 158
 
13.4%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
3
995 
4
181 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row4
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 995
84.6%
4 181
 
15.4%

Length

2023-02-06T06:30:26.405386image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T06:30:26.730053image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
3 995
84.6%
4 181
 
15.4%

Most occurring characters

ValueCountFrequency (%)
3 995
84.6%
4 181
 
15.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 995
84.6%
4 181
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 995
84.6%
4 181
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 995
84.6%
4 181
 
15.4%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
3
359 
4
347 
2
249 
1
221 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
3 359
30.5%
4 347
29.5%
2 249
21.2%
1 221
18.8%

Length

2023-02-06T06:30:27.128432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T06:30:27.593571image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
3 359
30.5%
4 347
29.5%
2 249
21.2%
1 221
18.8%

Most occurring characters

ValueCountFrequency (%)
3 359
30.5%
4 347
29.5%
2 249
21.2%
1 221
18.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 359
30.5%
4 347
29.5%
2 249
21.2%
1 221
18.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 359
30.5%
4 347
29.5%
2 249
21.2%
1 221
18.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 359
30.5%
4 347
29.5%
2 249
21.2%
1 221
18.8%

StockOptionLevel
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
0
498 
1
480 
2
131 
3
67 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 498
42.3%
1 480
40.8%
2 131
 
11.1%
3 67
 
5.7%

Length

2023-02-06T06:30:27.988561image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T06:30:28.426651image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 498
42.3%
1 480
40.8%
2 131
 
11.1%
3 67
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0 498
42.3%
1 480
40.8%
2 131
 
11.1%
3 67
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 498
42.3%
1 480
40.8%
2 131
 
11.1%
3 67
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 498
42.3%
1 480
40.8%
2 131
 
11.1%
3 67
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 498
42.3%
1 480
40.8%
2 131
 
11.1%
3 67
 
5.7%

TotalWorkingYears
Real number (ℝ)

Distinct40
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.269558
Minimum0
Maximum40
Zeros9
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2023-02-06T06:30:28.923216image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median10
Q315
95-th percentile27
Maximum40
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.6579843
Coefficient of variation (CV)0.67952837
Kurtosis0.78720624
Mean11.269558
Median Absolute Deviation (MAD)4
Skewness1.0660422
Sum13253
Variance58.644724
MonotonicityNot monotonic
2023-02-06T06:30:29.568439image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10 160
 
13.6%
6 99
 
8.4%
8 84
 
7.1%
9 75
 
6.4%
5 67
 
5.7%
7 65
 
5.5%
1 64
 
5.4%
4 49
 
4.2%
12 39
 
3.3%
3 37
 
3.1%
Other values (30) 437
37.2%
ValueCountFrequency (%)
0 9
 
0.8%
1 64
5.4%
2 23
 
2.0%
3 37
 
3.1%
4 49
4.2%
5 67
5.7%
6 99
8.4%
7 65
5.5%
8 84
7.1%
9 75
6.4%
ValueCountFrequency (%)
40 1
 
0.1%
38 1
 
0.1%
37 2
 
0.2%
36 5
0.4%
35 1
 
0.1%
34 3
 
0.3%
33 6
0.5%
32 5
0.4%
31 8
0.7%
30 5
0.4%

TrainingTimesLastYear
Real number (ℝ)

Distinct7
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7857143
Minimum0
Maximum6
Zeros41
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2023-02-06T06:30:30.073350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile5
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2716215
Coefficient of variation (CV)0.45647952
Kurtosis0.61951249
Mean2.7857143
Median Absolute Deviation (MAD)1
Skewness0.60324086
Sum3276
Variance1.6170213
MonotonicityNot monotonic
2023-02-06T06:30:30.478210image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 450
38.3%
3 395
33.6%
4 95
 
8.1%
5 89
 
7.6%
1 54
 
4.6%
6 52
 
4.4%
0 41
 
3.5%
ValueCountFrequency (%)
0 41
 
3.5%
1 54
 
4.6%
2 450
38.3%
3 395
33.6%
4 95
 
8.1%
5 89
 
7.6%
6 52
 
4.4%
ValueCountFrequency (%)
6 52
 
4.4%
5 89
 
7.6%
4 95
 
8.1%
3 395
33.6%
2 450
38.3%
1 54
 
4.6%
0 41
 
3.5%

WorkLifeBalance
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
3
711 
2
274 
4
125 
1
 
66

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row3
4th row4
5th row3

Common Values

ValueCountFrequency (%)
3 711
60.5%
2 274
 
23.3%
4 125
 
10.6%
1 66
 
5.6%

Length

2023-02-06T06:30:31.352589image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T06:30:31.757702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
3 711
60.5%
2 274
 
23.3%
4 125
 
10.6%
1 66
 
5.6%

Most occurring characters

ValueCountFrequency (%)
3 711
60.5%
2 274
 
23.3%
4 125
 
10.6%
1 66
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 711
60.5%
2 274
 
23.3%
4 125
 
10.6%
1 66
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 711
60.5%
2 274
 
23.3%
4 125
 
10.6%
1 66
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 711
60.5%
2 274
 
23.3%
4 125
 
10.6%
1 66
 
5.6%

YearsInCurrentRole
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2355442
Minimum0
Maximum18
Zeros193
Zeros (%)16.4%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2023-02-06T06:30:32.075229image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q37
95-th percentile11
Maximum18
Range18
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.6474129
Coefficient of variation (CV)0.86114385
Kurtosis0.5713178
Mean4.2355442
Median Absolute Deviation (MAD)3
Skewness0.94752544
Sum4981
Variance13.303621
MonotonicityNot monotonic
2023-02-06T06:30:32.406904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2 296
25.2%
0 193
16.4%
7 178
15.1%
3 106
 
9.0%
4 89
 
7.6%
8 71
 
6.0%
9 50
 
4.3%
1 50
 
4.3%
6 28
 
2.4%
5 26
 
2.2%
Other values (9) 89
 
7.6%
ValueCountFrequency (%)
0 193
16.4%
1 50
 
4.3%
2 296
25.2%
3 106
 
9.0%
4 89
 
7.6%
5 26
 
2.2%
6 28
 
2.4%
7 178
15.1%
8 71
 
6.0%
9 50
 
4.3%
ValueCountFrequency (%)
18 2
 
0.2%
17 4
 
0.3%
16 6
 
0.5%
15 6
 
0.5%
14 9
 
0.8%
13 11
 
0.9%
12 8
 
0.7%
11 18
 
1.5%
10 25
2.1%
9 50
4.3%

YearsSinceLastPromotion
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.172619
Minimum0
Maximum15
Zeros459
Zeros (%)39.0%
Negative0
Negative (%)0.0%
Memory size18.4 KiB
2023-02-06T06:30:32.866396image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile9
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.1773076
Coefficient of variation (CV)1.462432
Kurtosis3.7117997
Mean2.172619
Median Absolute Deviation (MAD)1
Skewness1.9887353
Sum2555
Variance10.095284
MonotonicityNot monotonic
2023-02-06T06:30:33.226542image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 459
39.0%
1 291
24.7%
2 130
 
11.1%
7 59
 
5.0%
4 51
 
4.3%
3 38
 
3.2%
5 36
 
3.1%
6 27
 
2.3%
11 18
 
1.5%
8 17
 
1.4%
Other values (6) 50
 
4.3%
ValueCountFrequency (%)
0 459
39.0%
1 291
24.7%
2 130
 
11.1%
3 38
 
3.2%
4 51
 
4.3%
5 36
 
3.1%
6 27
 
2.3%
7 59
 
5.0%
8 17
 
1.4%
9 15
 
1.3%
ValueCountFrequency (%)
15 11
 
0.9%
14 7
 
0.6%
13 5
 
0.4%
12 7
 
0.6%
11 18
 
1.5%
10 5
 
0.4%
9 15
 
1.3%
8 17
 
1.4%
7 59
5.0%
6 27
2.3%

Attrition_1
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
0
997 
1
179 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 997
84.8%
1 179
 
15.2%

Length

2023-02-06T06:30:33.614591image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T06:30:34.114538image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 997
84.8%
1 179
 
15.2%

Most occurring characters

ValueCountFrequency (%)
0 997
84.8%
1 179
 
15.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 997
84.8%
1 179
 
15.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 997
84.8%
1 179
 
15.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 997
84.8%
1 179
 
15.2%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
0
954 
1
222 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 954
81.1%
1 222
 
18.9%

Length

2023-02-06T06:30:34.467046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T06:30:34.894494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 954
81.1%
1 222
 
18.9%

Most occurring characters

ValueCountFrequency (%)
0 954
81.1%
1 222
 
18.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 954
81.1%
1 222
 
18.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 954
81.1%
1 222
 
18.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 954
81.1%
1 222
 
18.9%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
1
822 
0
354 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 822
69.9%
0 354
30.1%

Length

2023-02-06T06:30:35.133208image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T06:30:35.479530image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
1 822
69.9%
0 354
30.1%

Most occurring characters

ValueCountFrequency (%)
1 822
69.9%
0 354
30.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 822
69.9%
0 354
30.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 822
69.9%
0 354
30.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 822
69.9%
0 354
30.1%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
0
688 
1
488 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 688
58.5%
1 488
41.5%

Length

2023-02-06T06:30:35.791975image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T06:30:36.099114image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 688
58.5%
1 488
41.5%

Most occurring characters

ValueCountFrequency (%)
0 688
58.5%
1 488
41.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 688
58.5%
1 488
41.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 688
58.5%
1 488
41.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 688
58.5%
1 488
41.5%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
0
1052 
1
124 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1052
89.5%
1 124
 
10.5%

Length

2023-02-06T06:30:36.334190image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T06:30:36.659369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1052
89.5%
1 124
 
10.5%

Most occurring characters

ValueCountFrequency (%)
0 1052
89.5%
1 124
 
10.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1052
89.5%
1 124
 
10.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1052
89.5%
1 124
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1052
89.5%
1 124
 
10.5%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
0
802 
1
374 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 802
68.2%
1 374
31.8%

Length

2023-02-06T06:30:36.950088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T06:30:37.363566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 802
68.2%
1 374
31.8%

Most occurring characters

ValueCountFrequency (%)
0 802
68.2%
1 374
31.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 802
68.2%
1 374
31.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 802
68.2%
1 374
31.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 802
68.2%
1 374
31.8%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
0
1109 
1
 
67

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1109
94.3%
1 67
 
5.7%

Length

2023-02-06T06:30:37.845732image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T06:30:38.281809image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1109
94.3%
1 67
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0 1109
94.3%
1 67
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1109
94.3%
1 67
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1109
94.3%
1 67
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1109
94.3%
1 67
 
5.7%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
0
1071 
1
 
105

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1071
91.1%
1 105
 
8.9%

Length

2023-02-06T06:30:38.637572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T06:30:38.921376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1071
91.1%
1 105
 
8.9%

Most occurring characters

ValueCountFrequency (%)
0 1071
91.1%
1 105
 
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1071
91.1%
1 105
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1071
91.1%
1 105
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1071
91.1%
1 105
 
8.9%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
0
1138 
1
 
38

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1138
96.8%
1 38
 
3.2%

Length

2023-02-06T06:30:39.132217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T06:30:39.472153image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1138
96.8%
1 38
 
3.2%

Most occurring characters

ValueCountFrequency (%)
0 1138
96.8%
1 38
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1138
96.8%
1 38
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1138
96.8%
1 38
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1138
96.8%
1 38
 
3.2%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
0
966 
1
210 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 966
82.1%
1 210
 
17.9%

Length

2023-02-06T06:30:39.801385image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T06:30:40.142518image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 966
82.1%
1 210
 
17.9%

Most occurring characters

ValueCountFrequency (%)
0 966
82.1%
1 210
 
17.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 966
82.1%
1 210
 
17.9%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 966
82.1%
1 210
 
17.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 966
82.1%
1 210
 
17.9%

JobRole_Manager
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
0
1094 
1
 
82

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1094
93.0%
1 82
 
7.0%

Length

2023-02-06T06:30:40.400232image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T06:30:41.007354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1094
93.0%
1 82
 
7.0%

Most occurring characters

ValueCountFrequency (%)
0 1094
93.0%
1 82
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1094
93.0%
1 82
 
7.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1094
93.0%
1 82
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1094
93.0%
1 82
 
7.0%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
0
1057 
1
119 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1057
89.9%
1 119
 
10.1%

Length

2023-02-06T06:30:41.579989image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T06:30:41.995849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1057
89.9%
1 119
 
10.1%

Most occurring characters

ValueCountFrequency (%)
0 1057
89.9%
1 119
 
10.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1057
89.9%
1 119
 
10.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1057
89.9%
1 119
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1057
89.9%
1 119
 
10.1%

JobRole_Research Director
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
0
1112 
1
 
64

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1112
94.6%
1 64
 
5.4%

Length

2023-02-06T06:30:42.395398image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T06:30:42.787012image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1112
94.6%
1 64
 
5.4%

Most occurring characters

ValueCountFrequency (%)
0 1112
94.6%
1 64
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1112
94.6%
1 64
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1112
94.6%
1 64
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1112
94.6%
1 64
 
5.4%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
0
947 
1
229 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 947
80.5%
1 229
 
19.5%

Length

2023-02-06T06:30:43.004382image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T06:30:43.269950image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 947
80.5%
1 229
 
19.5%

Most occurring characters

ValueCountFrequency (%)
0 947
80.5%
1 229
 
19.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 947
80.5%
1 229
 
19.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 947
80.5%
1 229
 
19.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 947
80.5%
1 229
 
19.5%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
0
916 
1
260 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 916
77.9%
1 260
 
22.1%

Length

2023-02-06T06:30:43.468975image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T06:30:44.370957image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 916
77.9%
1 260
 
22.1%

Most occurring characters

ValueCountFrequency (%)
0 916
77.9%
1 260
 
22.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 916
77.9%
1 260
 
22.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 916
77.9%
1 260
 
22.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 916
77.9%
1 260
 
22.1%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
0
1110 
1
 
66

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 1110
94.4%
1 66
 
5.6%

Length

2023-02-06T06:30:44.584800image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T06:30:44.804752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1110
94.4%
1 66
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 1110
94.4%
1 66
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1110
94.4%
1 66
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1110
94.4%
1 66
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1110
94.4%
1 66
 
5.6%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
0
632 
1
544 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 632
53.7%
1 544
46.3%

Length

2023-02-06T06:30:45.001086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T06:30:45.267745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 632
53.7%
1 544
46.3%

Most occurring characters

ValueCountFrequency (%)
0 632
53.7%
1 544
46.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 632
53.7%
1 544
46.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 632
53.7%
1 544
46.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 632
53.7%
1 544
46.3%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
0
804 
1
372 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 804
68.4%
1 372
31.6%

Length

2023-02-06T06:30:45.629672image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T06:30:45.900234image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 804
68.4%
1 372
31.6%

Most occurring characters

ValueCountFrequency (%)
0 804
68.4%
1 372
31.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 804
68.4%
1 372
31.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 804
68.4%
1 372
31.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 804
68.4%
1 372
31.6%

OverTime_1
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size18.4 KiB
0
847 
1
329 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1176
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 847
72.0%
1 329
 
28.0%

Length

2023-02-06T06:30:46.137928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-06T06:30:46.391732image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 847
72.0%
1 329
 
28.0%

Most occurring characters

ValueCountFrequency (%)
0 847
72.0%
1 329
 
28.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1176
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 847
72.0%
1 329
 
28.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1176
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 847
72.0%
1 329
 
28.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1176
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 847
72.0%
1 329
 
28.0%

Interactions

2023-02-06T06:30:08.024850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:10.309958image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:16.594736image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:21.578645image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:26.329719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:31.952956image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:38.858572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:44.101218image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:49.304358image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:55.564522image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:30:01.840460image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:30:08.365223image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:10.779381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:17.008912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:22.035902image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:26.674954image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:32.534163image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:39.293997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:44.526742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:50.003174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:56.234324image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:30:02.353078image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:30:08.910715image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:11.353049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:17.425971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:22.590668image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:27.034357image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:33.154819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:39.765589image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:44.965985image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:50.614441image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:56.898929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:30:02.865552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:30:09.449266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:11.806873image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:17.990845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:23.073645image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:27.466195image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:33.862277image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:40.167771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:45.349250image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:51.164769image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:57.574888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:30:03.457837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:30:10.021180image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:13.113056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:18.405991image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:23.517605image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:28.462471image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:34.457487image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:40.671401image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:45.902797image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:51.851624image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:58.261301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:30:04.037308image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:30:10.480190image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:13.700955image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:18.847355image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:23.919274image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:28.981915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:35.215146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:41.204974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:46.978851image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:52.365817image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:58.741669image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:30:05.176506image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:30:10.896466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:14.313393image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:19.278066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:24.252617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:29.390888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:35.769842image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:41.693178image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:47.349733image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:52.836044image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:59.150159image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:30:05.579951image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:30:11.298963image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:14.854379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:19.667357image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:24.617729image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:29.765615image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:36.481812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:42.222799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:47.757143image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:53.336591image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:59.587864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:30:06.086697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:30:11.905738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:15.352255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:20.043086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:25.085548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:30.190133image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:37.229808image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:42.716923image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:48.225019image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:53.796286image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:30:00.286046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:30:06.589244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:30:12.326565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:15.788035image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:20.600678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:25.475209image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:30.752102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:37.693503image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:43.237713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:48.575155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:54.323422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:30:00.744713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:30:07.115248image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:30:12.779067image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:16.204440image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:21.087373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:25.966019image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:31.368313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:38.264989image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:43.634547image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:48.894648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:29:54.890781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:30:01.340459image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-06T06:30:07.635829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-02-06T06:30:46.729481image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
AgeDailyRateDistanceFromHomeHourlyRateMonthlyIncomeMonthlyRateNumCompaniesWorkedTotalWorkingYearsTrainingTimesLastYearYearsInCurrentRoleYearsSinceLastPromotionEducationEnvironmentSatisfactionGenderJobInvolvementJobLevelPerformanceRatingRelationshipSatisfactionStockOptionLevelWorkLifeBalanceAttrition_1BusinessTravel_Travel_FrequentlyBusinessTravel_Travel_RarelyEducationField_Life SciencesEducationField_MarketingEducationField_MedicalEducationField_OtherEducationField_Technical DegreeJobRole_Human ResourcesJobRole_Laboratory TechnicianJobRole_ManagerJobRole_Manufacturing DirectorJobRole_Research DirectorJobRole_Research ScientistJobRole_Sales ExecutiveJobRole_Sales RepresentativeMaritalStatus_MarriedMaritalStatus_SingleOverTime_1
Age1.0000.017-0.0250.0560.4680.0030.3600.6470.0030.1850.1550.1570.0000.0000.0200.3010.0000.0510.0970.0240.2180.0000.0390.0000.0000.0000.0560.0400.0160.1310.3180.0740.2060.1530.1260.2340.1090.1940.054
DailyRate0.0171.000-0.0020.0280.030-0.0200.0490.033-0.0130.008-0.0190.0360.0000.0540.0000.0290.0000.0000.0000.0370.0660.0000.0000.0000.0560.0000.0000.0850.0000.0630.0000.0000.0370.0000.0000.0000.1160.0900.000
DistanceFromHome-0.025-0.0021.0000.0140.0140.042-0.014-0.007-0.0310.014-0.0010.0000.0000.0130.0520.0340.0870.0000.0000.0000.0520.0000.0580.0000.0420.0000.0000.0700.0000.0000.0000.0000.0630.0120.0320.0000.0000.0350.027
HourlyRate0.0560.0280.0141.000-0.002-0.0190.0440.018-0.001-0.033-0.0490.0000.0000.0000.0000.0000.0000.0430.0580.0000.0660.0000.0430.0630.0940.0000.0340.0450.0630.0510.0800.0000.0370.0000.0000.0500.0000.0090.064
MonthlyIncome0.4680.0300.014-0.0021.0000.0440.2010.710-0.0250.3870.2750.0860.0000.0670.0520.8740.0290.0640.0270.0000.1950.0590.0550.0000.1310.0350.0410.0310.0660.3780.7070.2710.5580.3940.4550.3070.0420.0350.000
MonthlyRate0.003-0.0200.042-0.0190.0441.000-0.014-0.009-0.014-0.012-0.0090.0000.0000.0000.0150.0000.0000.0590.0000.0000.0000.0000.0000.0450.0000.0000.0510.0000.0000.0000.0000.0000.0000.0000.0000.0220.0260.0000.000
NumCompaniesWorked0.3600.049-0.0140.0440.201-0.0141.0000.324-0.028-0.125-0.0510.0850.0000.0000.0000.1170.0000.0000.0000.0400.0800.0000.0000.1070.0700.0680.0340.0000.0440.0780.1030.0540.1160.0650.0650.0980.0490.0480.000
TotalWorkingYears0.6470.033-0.0070.0180.710-0.0090.3241.000-0.0080.4840.3260.1060.0000.0000.0000.5410.0000.0000.0320.0000.1930.0110.0330.0280.0000.0760.0000.0000.0000.2170.5600.1010.3670.2350.1990.3520.0570.0870.000
TrainingTimesLastYear0.003-0.013-0.031-0.001-0.025-0.014-0.028-0.0081.000-0.0060.0050.0180.0000.0000.0110.0190.0000.0000.0000.0000.0730.0000.0000.0510.0730.1240.0000.0340.0000.0580.0000.0680.0000.0440.0000.0440.0480.0000.088
YearsInCurrentRole0.1850.0080.014-0.0330.387-0.012-0.1250.484-0.0061.0000.5070.0270.0520.0680.0000.2370.0590.0420.0000.0280.1670.0000.0000.0000.0480.0000.0000.0000.0000.1330.2020.0300.1890.1020.0940.1540.0490.0650.076
YearsSinceLastPromotion0.155-0.019-0.001-0.0490.275-0.009-0.0510.3260.0050.5071.0000.0330.0000.0220.0000.2030.0000.0550.0450.0000.0190.1030.0510.0000.0150.0000.0000.0000.0000.0850.2600.0470.0710.0890.0000.0000.0310.0000.000
Education0.1570.0360.0000.0000.0860.0000.0850.1060.0180.0270.0331.0000.0160.0000.0290.0820.0000.0000.0350.0000.0320.0000.0000.0000.0980.0530.0720.0000.0420.0550.0060.0000.0000.0000.0400.0920.0280.0170.033
EnvironmentSatisfaction0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0520.0000.0161.0000.0000.0350.0290.0040.0000.0000.0000.1310.0000.0270.0290.0000.0720.0000.0000.0600.0000.0000.0630.0370.0000.0000.0200.0370.0000.058
Gender0.0000.0540.0130.0000.0670.0000.0000.0000.0000.0680.0220.0000.0001.0000.0000.0490.0170.0000.0000.0000.0000.0000.0000.0000.0250.0000.0000.0000.0360.0790.0250.0620.0000.0000.0000.0530.0070.0130.049
JobInvolvement0.0200.0000.0520.0000.0520.0150.0000.0000.0110.0000.0000.0290.0350.0001.0000.0230.0000.0000.0520.0000.1060.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.0000.0320.000
JobLevel0.3010.0290.0340.0000.8740.0000.1170.5410.0190.2370.2030.0820.0290.0490.0231.0000.0000.0300.0610.0000.2110.0000.0290.0000.1490.0420.0000.0540.0760.4120.6390.2880.4800.4400.4760.2740.0420.0560.000
PerformanceRating0.0000.0000.0870.0000.0290.0000.0000.0000.0000.0590.0000.0000.0040.0170.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0350.0000.0110.0000.0000.0000.000
RelationshipSatisfaction0.0510.0000.0000.0430.0640.0590.0000.0000.0000.0420.0550.0000.0000.0000.0000.0300.0001.0000.0190.0000.0740.0000.0320.0000.0540.0440.0000.0000.0000.0670.0280.0530.0000.0000.0590.0000.0000.0230.017
StockOptionLevel0.0970.0000.0000.0580.0270.0000.0000.0320.0000.0000.0450.0350.0000.0000.0520.0610.0000.0191.0000.0000.1850.0000.0000.0470.0000.0840.0000.0000.0000.0560.0760.0000.0000.0020.0000.0000.4090.7920.000
WorkLifeBalance0.0240.0370.0000.0000.0000.0000.0400.0000.0000.0280.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.1190.0000.0000.0600.0000.0280.0230.0310.0480.0000.0000.0000.0220.0620.0000.0120.0000.0000.000
Attrition_10.2180.0660.0520.0660.1950.0000.0800.1930.0730.1670.0190.0320.1310.0000.1060.2110.0000.0740.1850.1191.0000.1090.0400.0000.0420.0240.0000.0550.0000.0920.0580.0520.0810.0000.0000.1560.0770.1550.211
BusinessTravel_Travel_Frequently0.0000.0000.0000.0000.0590.0000.0000.0110.0000.0000.1030.0000.0000.0000.0000.0000.0000.0000.0000.0000.1091.0000.7320.0560.0190.0000.0000.0150.0000.0000.0420.0000.0000.0000.0180.0600.0000.0000.000
BusinessTravel_Travel_Rarely0.0390.0000.0580.0430.0550.0000.0000.0330.0000.0000.0510.0000.0270.0000.0000.0290.0000.0320.0000.0000.0400.7321.0000.0510.0370.0000.0000.0000.0000.0000.0090.0000.0260.0000.0000.0020.0250.0000.000
EducationField_Life Sciences0.0000.0000.0000.0630.0000.0450.1070.0280.0510.0000.0000.0000.0290.0000.0000.0000.0000.0000.0470.0600.0000.0560.0511.0000.2850.5730.2010.2590.0420.0160.0000.0190.0000.0000.0620.0330.0000.0080.000
EducationField_Marketing0.0000.0560.0420.0940.1310.0000.0700.0000.0730.0480.0150.0980.0000.0250.0110.1490.0000.0540.0000.0000.0420.0190.0370.2851.0000.2300.0730.0980.0470.1540.0300.1070.0700.1630.4200.1480.0000.0000.000
EducationField_Medical0.0000.0000.0000.0000.0350.0000.0680.0760.1240.0000.0000.0530.0720.0000.0000.0420.0000.0440.0840.0280.0240.0000.0000.5730.2301.0000.1610.2090.0000.0480.0000.0440.0500.0100.1250.0200.0000.0000.000
EducationField_Other0.0560.0000.0000.0340.0410.0510.0340.0000.0000.0000.0000.0720.0000.0000.0000.0000.0000.0000.0000.0230.0000.0000.0000.2010.0730.1611.0000.0640.0000.0550.0000.0000.0000.0000.0370.0210.0000.0000.000
EducationField_Technical Degree0.0400.0850.0700.0450.0310.0000.0000.0000.0340.0000.0000.0000.0000.0000.0000.0540.0000.0000.0000.0310.0550.0150.0000.2590.0980.2090.0641.0000.0000.0000.0160.0000.0000.0700.0380.0000.0000.0000.000
JobRole_Human Resources0.0160.0000.0000.0630.0660.0000.0440.0000.0000.0000.0000.0420.0600.0360.0000.0760.0000.0000.0000.0480.0000.0000.0000.0420.0470.0000.0000.0001.0000.0730.0280.0450.0160.0790.0870.0180.0240.0720.000
JobRole_Laboratory Technician0.1310.0630.0000.0510.3780.0000.0780.2170.0580.1330.0850.0550.0000.0790.0000.4120.0000.0670.0560.0000.0920.0000.0000.0160.1540.0480.0550.0000.0731.0000.1200.1500.1030.2250.2440.1050.0000.0000.041
JobRole_Manager0.3180.0000.0000.0800.7070.0000.1030.5600.0000.2020.2600.0060.0000.0250.0000.6390.0210.0280.0760.0000.0580.0420.0090.0000.0300.0000.0000.0160.0280.1201.0000.0810.0510.1270.1390.0520.0490.0130.000
JobRole_Manufacturing Director0.0740.0000.0000.0000.2710.0000.0540.1010.0680.0300.0470.0000.0630.0620.0000.2880.0000.0530.0000.0000.0520.0000.0000.0190.1070.0440.0000.0000.0450.1500.0811.0000.0680.1590.1730.0700.0000.0000.000
JobRole_Research Director0.2060.0370.0630.0370.5580.0000.1160.3670.0000.1890.0710.0000.0370.0000.0000.4800.0350.0000.0000.0220.0810.0000.0260.0000.0700.0500.0000.0000.0160.1030.0510.0681.0000.1090.1200.0410.0000.0080.000
JobRole_Research Scientist0.1530.0000.0120.0000.3940.0000.0650.2350.0440.1020.0890.0000.0000.0000.0240.4400.0000.0000.0020.0620.0000.0000.0000.0000.1630.0100.0000.0700.0790.2250.1270.1590.1091.0000.2580.1120.0280.0150.028
JobRole_Sales Executive0.1260.0000.0320.0000.4550.0000.0650.1990.0000.0940.0000.0400.0000.0000.0000.4760.0110.0590.0000.0000.0000.0180.0000.0620.4200.1250.0370.0380.0870.2440.1390.1730.1200.2581.0000.1220.0000.0000.000
JobRole_Sales Representative0.2340.0000.0000.0500.3070.0220.0980.3520.0440.1540.0000.0920.0200.0530.0000.2740.0000.0000.0000.0120.1560.0600.0020.0330.1480.0200.0210.0000.0180.1050.0520.0700.0410.1120.1221.0000.0000.0440.000
MaritalStatus_Married0.1090.1160.0000.0000.0420.0260.0490.0570.0480.0490.0310.0280.0370.0070.0000.0420.0000.0000.4090.0000.0770.0000.0250.0000.0000.0000.0000.0000.0240.0000.0490.0000.0000.0280.0000.0001.0000.6290.002
MaritalStatus_Single0.1940.0900.0350.0090.0350.0000.0480.0870.0000.0650.0000.0170.0000.0130.0320.0560.0000.0230.7920.0000.1550.0000.0000.0080.0000.0000.0000.0000.0720.0000.0130.0000.0080.0150.0000.0440.6291.0000.000
OverTime_10.0540.0000.0270.0640.0000.0000.0000.0000.0880.0760.0000.0330.0580.0490.0000.0000.0000.0170.0000.0000.2110.0000.0000.0000.0000.0000.0000.0000.0000.0410.0000.0000.0000.0280.0000.0000.0020.0001.000

Missing values

2023-02-06T06:30:13.941141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-06T06:30:16.024854image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AgeDailyRateDistanceFromHomeEducationEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelMonthlyIncomeMonthlyRateNumCompaniesWorkedPerformanceRatingRelationshipSatisfactionStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsInCurrentRoleYearsSinceLastPromotionAttrition_1BusinessTravel_Travel_FrequentlyBusinessTravel_Travel_RarelyEducationField_Life SciencesEducationField_MarketingEducationField_MedicalEducationField_OtherEducationField_Technical DegreeJobRole_Human ResourcesJobRole_Laboratory TechnicianJobRole_ManagerJobRole_Manufacturing DirectorJobRole_Research DirectorJobRole_Research ScientistJobRole_Sales ExecutiveJobRole_Sales RepresentativeMaritalStatus_MarriedMaritalStatus_SingleOverTime_1
63825583413187224256181541310514200010100000000010100
1356413378330543243932684154311433410010100000000010100
49434204143303131257929121342833200010000100000001001
1056281496131192312909157473341534211100000100000001100
80545105094206522559317970134115231040001000000000010100
50032646941092326322180891341622400011000000000100101
11764930122410723416413349833222723210010001000001000100
61426887523088212366208981311823711100010000000100101
1251309791523194237140308823111223710010100000000010000
1426322672943049212837159191330633240011000001000000010
AgeDailyRateDistanceFromHomeEducationEnvironmentSatisfactionGenderHourlyRateJobInvolvementJobLevelMonthlyIncomeMonthlyRateNumCompaniesWorkedPerformanceRatingRelationshipSatisfactionStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsInCurrentRoleYearsSinceLastPromotionAttrition_1BusinessTravel_Travel_FrequentlyBusinessTravel_Travel_RarelyEducationField_Life SciencesEducationField_MarketingEducationField_MedicalEducationField_OtherEducationField_Technical DegreeJobRole_Human ResourcesJobRole_Laboratory TechnicianJobRole_ManagerJobRole_Manufacturing DirectorJobRole_Research DirectorJobRole_Research ScientistJobRole_Sales ExecutiveJobRole_Sales RepresentativeMaritalStatus_MarriedMaritalStatus_SingleOverTime_1
105534829153217134170071192973421632860100010000001000000
5453050127531993253042527574411022770010100000000010000
18134629272409531231157112340933210010010000000100010
1464261167534030212966213780340523200010001000000001010
137749106421214235191611373833402833440101000000001000100
3533713196331514259741700143121323760010010000000100001
1166483654531892415202560224212333220100010000100000100
108251280714164212889268971332223220010010000000100100
983344042430983266876163134014241140010000100000000010
83041167124214631476690513311643000011000001000000101